Oiling the Wheels of Change: The Role of Adaptive Automatic Problem Decomposition in Non--Stationary Environments
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Genetic algorithms (GAs) that solve hard problems quickly, reliably and accurately are called competent GAs. When the fitness landscape of a problem changes overtime, the problem is called non--stationary, dynamic or time--variant problem. This paper investigates the use of competent GAs for optimizing non--stationary optimization problems. More specifically, we use an information theoretic approach based on the minimum description length principle to adaptively identify regularities and substructures that can be exploited to respond quickly to changes in the environment. We also develop a special type of problems with bounded difficulties to test non--stationary optimization problems. The results provide new insights into non-stationary optimization problems and show that a search algorithm which automatically identifies and exploits possible decompositions is more robust and responds quickly to changes than a simple genetic algorithm.
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